Electronic Meeting Intelligence

ABSTRACT

Techniques related to electronic meeting intelligence are disclosed. An apparatus receives audio/video data including first meeting content data for an electronic meeting that includes multiple participants. The apparatus extracts the first meeting content data from the audio/video data. The apparatus generates meeting content metadata based on analyzing the first meeting content data. The apparatus includes the meeting content metadata in a report of the electronic meeting. If the apparatus determines that the audio/video data includes a cue for the apparatus to intervene in the electronic meeting, the apparatus generates intervention data including second meeting content data that is different from the first meeting content data. During the electronic meeting, the apparatus sends the intervention data to one or more nodes associated with at least one participant of the multiple participants.

PRIORITY CLAIM

This application is a Continuation of prior U.S. patent application Ser.No. 15/477,240 (Attorney Docket No. 49986-0902) entitled “ElectronicMeeting Intelligence”, filed Apr. 3, 2017, which is a Continuation ofprior U.S. patent application Ser. No. 14/992,273 (Attorney Docket No.49986-0859) entitled “Electronic Meeting Intelligence”, filed Jan. 11,2016, which claims the benefit of a Provisional Application No.62/253,325 (Attorney Docket No. 49986-0851) entitled “Electronic MeetingIntelligence”, filed Nov. 10, 2015, the entire contents all of which arehereby incorporated by reference as if fully set forth herein, under 35U.S.C. § 119(e).

This application is related to:

-   -   U.S. patent application Ser. No. 15/477,276 (Attorney Docket No.        49986-0903) entitled “Electronic Meeting Intelligence”, filed        Apr. 3, 2017;    -   U.S. patent application Ser. No. 14/992,278 (Attorney Docket No.        49986-0860) entitled “Electronic Meeting Intelligence”, filed        Jan. 11, 2016;    -   U.S. patent application Ser. No. 15/290,855 (Attorney Docket No.        49986-0888) entitled “Managing Electronic Meetings Using        Artificial Intelligence and Meeting Rules Templates”, filed Oct.        11, 2016;    -   U.S. patent application Ser. No. 15/290,856 (Attorney Docket No.        49986-0889) entitled “Creating Agendas for Electronic Meetings        Using Artificial Intelligence”, filed Oct. 11, 2016;    -   U.S. patent application Ser. No. 15/290,858 (Attorney Docket No.        49986-0890) entitled “Selecting Meeting Participants for        Electronic Meetings Using Artificial Intelligence”, filed Oct.        11, 2016;    -   U.S. patent application Ser. No. 15/290,860 (Attorney Docket No.        49986-0891) entitled “Real-Time (Intra-Meeting) Processing Using        Artificial Intelligence”, filed Oct. 11, 2016;    -   U.S. patent application Ser. No. 15/290,861 (Attorney Docket No.        49986-0892) entitled “Post-Meeting Processing Using Artificial        Intelligence”, filed Oct. 11, 2016; the contents all of which        are incorporated by reference in their entirety for all purposes        as if fully set forth herein.

FIELD OF THE DISCLOSURE

Embodiments relate to artificial intelligence and more specifically, toelectronic meeting intelligence.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

A meeting is typically an effective vehicle for coordinating thesuccessful accomplishment of a common goal shared by multiple people.However, a meeting can also devolve into a counterproductive use of timein the absence of proper organization of the meeting itself. Forexample, too much time may be devoted to a particular topic thatinvolves a small subset of meeting attendees, and this may result inwasted time for the remaining attendees. Such circumstances may beavoided through the use of a person serving as a meeting moderator, butpersonal biases may affect the neutrality of the person serving as themeeting moderator. Such circumstances may also be avoided throughadequate preparation for the meeting, but it may be impossible toforesee all the possible issues that may arise during the meeting.

Another way for a meeting to result in wasted time is by failing tofully reap the benefits provided by the meeting. For example,transcribing the meeting, scheduling an additional meeting, analyzingmeeting participation, and/or researching an issue that was contendedduring the meeting may be tedious follow-up actions that are neglectedafter the meeting. Even if the follow-up actions are performed, theprocess of performing them may be slow and cost-prohibitive.

Thus, it is desirable and beneficial to perform the administrativeduties related to a meeting using an approach without the aforementionedshortcomings.

SUMMARY

An apparatus includes one or more processors and one or morecomputer-readable media storing instructions which, when processed bythe one or more processors cause the apparatus to receive audio/videodata including first meeting content data for an electronic meeting thatincludes multiple participants. The instructions further cause theapparatus to determine that the audio/video data includes a cue for theapparatus to intervene in the electronic meeting. Further, theinstructions cause the apparatus to generate, in response to the cue,intervention data including second meeting content data that isdifferent from the first meeting content data. Still further, theinstructions cause the apparatus to send, during the electronic meeting,the intervention data to one or more nodes associated with at least oneparticipant of the multiple participants.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIGS. 1A-C depict example computer architectures upon which embodimentsmay be implemented.

FIG. 2 depicts an example participant interface.

FIG. 3 is a block diagram that depicts an arrangement for generatingintervention data.

FIGS. 4A-D depict examples of intervention data.

FIG. 5 is a block diagram that depicts an arrangement for generating areport.

FIGS. 6A-C depict examples of meeting content metadata.

FIGS. 7A-B depict example reports.

FIG. 8 is a flow diagram that depicts an approach for generatingintervention data.

FIG. 9 is a flow diagram that depicts an approach for generating areport.

FIG. 10 depicts an example computer system upon which embodiments may beimplemented.

While each of the drawing figures depicts a particular embodiment forpurposes of depicting a clear example, other embodiments may omit, addto, reorder, and/or modify any of the elements shown in the drawingfigures. For purposes of depicting clear examples, one or more figuresmay be described with reference to one or more other figures, but usingthe particular arrangement depicted in the one or more other figures isnot required in other embodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that the present disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent disclosure. Modifiers such as “first” and “second” may be usedto differentiate elements, but the modifiers do not necessarily indicateany particular order.

I. GENERAL OVERVIEW

II. NETWORK TOPOLOGY

-   -   A. MEETING INTELLIGENCE APPARATUS    -   B. NETWORK INFRASTRUCTURE    -   C. PARTICIPANT NODES

III. REAL-TIME PROCESSING

-   -   A. MEETING FLOW MANAGEMENT    -   B. INFORMATION RETRIEVAL SERVICES    -   C. MEETING CONTENT SUPPLEMENTATION    -   D. MEETING CONTENT METADATA GENERATION

IV. POST-PROCESSING

-   -   A. MEETING CONTENT ANALYSIS    -   B. MEETING SUMMARY    -   C. PARTICIPANT ANALYSIS

V. PROCESS OVERVIEW

-   -   A. GENERATING INTERVENTION DATA    -   B. GENERATING REPORTS

VI. IMPLEMENTATION MECHANISMS

I. General Overview

Artificial intelligence is introduced into an electronic meeting contextto perform various administrative tasks. The administrative tasksinclude tasks performed during an electronic meeting as well as after anelectronic meeting. The artificial intelligence performs theadministrative tasks based on analyzing meeting content using any of anumber of input detection tools. For example, the artificialintelligence can identify meeting participants, provide translationservices, respond to questions, and serve as a meeting moderator. Theartificial intelligence can also include elements of its meeting contentanalysis in various reports. For example, the reports can includemeeting transcripts, follow-up items, meeting efficiency metrics, andmeeting participant analyses.

II. Network Topology

FIGS. 1A-C depict example computer architectures upon which embodimentsmay be implemented. FIGS. 1A-C include various arrangements ofelectronic meeting 100. Electronic meeting 100 includes meetingintelligence apparatus 102 and one or more nodes 106A-N communicativelycoupled via network infrastructure 104. Nodes 106A-N are associated witha plurality of participants 108A-N.

Electronic meeting 100 may be an audioconferencing session, avideoconferencing session, and/or any other meeting involving datatransmissions between network infrastructure 104 and at least one node106A. Referring to FIGS. 1A-B, electronic meeting 100 includes a virtualgathering of participants 108A-N. In the examples of FIGS. 1A-B,participants 108A-N may be located in different physical locations yetcommunicate with each other via network infrastructure 104. Referring toFIG. 1C, electronic meeting 100 includes a physical gathering ofparticipants 108A-N. In the example of FIG. 1C, participants 108A-N maybe located in physical proximity to each other such that they maycommunicate with each other without network infrastructure 104. However,network infrastructure 104 may enable participants 108A-N to interactwith meeting intelligence apparatus 102, which receives input data fromand/or sends output data to node 106A.

In an embodiment, electronic meeting 100 involves a network ofcomputers. A “computer” may be one or more physical computers, virtualcomputers, and/or computing devices. A computer may be a client and/or aserver. Any reference to “a computer” herein may mean one or morecomputers, unless expressly stated otherwise. Each of the logical and/orfunctional units depicted in any of the figures or described herein maybe implemented using any of the techniques further described herein inconnection with FIG. 10.

A. Meeting Intelligence Apparatus

In an embodiment, meeting intelligence apparatus 102 is a computerendowed with artificial intelligence. The computer may be aspecial-purpose computer dedicated to providing artificial intelligenceto electronic meetings or a generic computer executing one or moreservices that provide artificial intelligence to electronic meetings. Inother words, meeting intelligence may be implemented using hardware,software, and/or firmware. Non-limiting examples include Ricoh Brain andIBM Watson. Meeting intelligence apparatus 102 may always be available(e.g., involve continuously running processes) or may be available ondemand (e.g., be powered on when needed). For example, meetingintelligence apparatus 102 may be replicated over multiple computerssuch that at any point in time, at least one computer can providemeeting intelligence services.

Meeting intelligence apparatus 102 can access meeting content data as ifit were a node associated with a participant in electronic meeting 100.Thus, meeting intelligence apparatus 102 may access any meeting contentdata that is transmitted from any of the one or more nodes 106A-Ninvolved in electronic meeting 100. For example, meeting intelligenceapparatus 102 may monitor, collect, and/or analyze all datatransmissions during electronic meeting 100.

Meeting intelligence apparatus 102 can analyze meeting content datausing any of a number of tools, such as speech or text recognition,voice or face identification, sentiment analysis, object detection,gestural analysis, thermal imaging, etc. Based on analyzing the meetingcontent data, meeting intelligence apparatus 102 performs any of anumber of automated tasks, such as providing a translation, respondingto an information request, moderating electronic meeting 100, generatinga report, etc.

Meeting intelligence apparatus 102 may be located at a number ofdifferent locations relative to network infrastructure 104. Referring toFIGS. 1A and 1C, meeting intelligence apparatus 102 is located outsidenetwork infrastructure 104. Referring to FIG. 1B, meeting intelligenceapparatus 102 is collocated with at least some of network infrastructure104.

In an embodiment, meeting intelligence apparatus 102 is communicativelycoupled to a meeting repository (not shown). The meeting repository maybe part of meeting intelligence apparatus 102 or may be located on aseparate device from meeting intelligence apparatus 102. The meetingrepository may be a database, a configuration file, and/or any othersystem or data structure that stores meeting data related to one or moreelectronic meetings. For example, meeting intelligence apparatus 102 maycollect and store, in the meeting repository, meeting content datarelated to multiple meetings. In other words, meeting intelligenceapparatus 102 can provide the services of a librarian formeeting-related data.

Like meeting intelligence apparatus 102, the meeting repository may belocated at a number of different locations relative to networkinfrastructure 104. For example, the meeting repository may be a datastructure stored in memory on one or more computers of networkinfrastructure 104.

In an embodiment, meeting intelligence apparatus 102 is communicativelycoupled to any of a number of external data sources (not shown), such aswebsites or databases managed by Salesforce, Oracle, SAP, Workday, orany entity other than the entity managing meeting intelligence apparatus102. Meeting intelligence apparatus 102 may be communicatively coupledto the external data sources via network infrastructure 104. Theexternal data sources may provide meeting intelligence apparatus 102with access to any of a variety of data, meeting-related or otherwise.

B. Network Infrastructure

Network infrastructure 104 may include any number and type of wired orwireless networks, such as local area networks (LANs), wide areanetworks (WANs), the Internet, etc. Network infrastructure 104 may alsoinclude one or more computers, such as one or more server computers,load-balancing computers, cloud-based computers, data centers, storagedevices, and/or any other special-purpose computing devices. Forexample, network infrastructure 104 may include a Unified CommunicationSystem (UCS) Service Platform by Ricoh Company Ltd., and/or any othercomputer(s) that manage(s) electronic meeting 100.

C. Participant Nodes

Each node of the one or more nodes 106A-N is associated with one or moreparticipants 108A-N. Each participant is a person who participates inelectronic meeting 100. Each node processes data transmission betweennetwork infrastructure 104 and at least one participant. Multiple nodes106A-N may be communicatively coupled with each other using any of anumber of different configurations. For example, multiple nodes may becommunicatively coupled with each other via a centralized server or viaa peer-to-peer network.

In an embodiment, a node includes a computer that executes an electronicmeeting application. The node may include a special-purpose computer,such as Ricoh UCS P3500, or a general-purpose computer that executes aspecial-purpose application, such as Ricoh UCS App. The node may alsoinclude any of a number of input/output mechanisms, such as a camera, amicrophone, and an electronic whiteboard. For example, the node mayinclude a smartphone with GPS capability, a camera, a microphone, anaccelerometer, a touchscreen, etc.

The input/output mechanisms may include a participant interface, such asa graphical user interface (GUI). FIG. 2 depicts an example participantinterface that is presented at node 106A. Referring to FIG. 2, node 106Aincludes a web-based interface that presents a variety of information toa participant during electronic meeting 100. The web-based interface ofFIG. 2 displays video streams including participant identification data206 associated with other participants, a meeting agenda managed bymeeting intelligence apparatus 102, and a message including schedulingindication 204. The meeting agenda includes agenda topic 202 and visualindication 200 of a current agenda topic. As shall be described ingreater detail hereafter, meeting intelligence apparatus 102 providesvisual indication 200, scheduling indication 204, and/or participantidentification data 206 based on analyzing meeting content data.

III. Real-Time Processing

Meeting intelligence apparatus 102 can intervene during electronicmeeting 100 to provide any of a variety of intervention data, such asvisual indication 200, scheduling indication 204, participantidentification data 206, recommendation information, and/or any otherdata that meeting intelligence apparatus 102 transmits during electronicmeeting 100. FIG. 3 is a block diagram that depicts an arrangement forgenerating intervention data. Referring to FIG. 3, meeting intelligenceapparatus 102 receives audio/video data 300 from node 106A. Audio/videodata 300 may be one or more data packets, a data stream, and/or anyother form of data that includes audio and/or video information relatedto electronic meeting 100. Audio/video data 300 includes first meetingcontent data 302 which, in turn, includes cue 304. Meeting intelligenceapparatus 102 includes cue detection logic 306, which determines whetheraudio/video data 300 includes cue 304. Meeting intelligence apparatus102 also includes data generation logic 308, which generatesintervention data 310 if audio/video data 300 includes cue 304. Meetingintelligence apparatus 102 sends intervention data 310 to node 106Aduring electronic meeting 100. Intervention data 310 includes secondmeeting content data 312.

Meeting intelligence apparatus 102 can intervene in electronic meeting100 in any of a number of ways. Non-limiting examples includeintervening to manage meeting flow, to provide information retrievalservices, and/or to supplement meeting content.

A. Meeting Flow Management

FIGS. 4A-B are block diagrams that depict arrangements for managingmeeting flow. Meeting intelligence apparatus 102 can manage meeting flowin any of a number of ways. For example, meeting intelligence apparatus102 can ensure that electronic meeting 100 follows a predeterminedmeeting schedule, such as a flowchart or a meeting agenda with arespective time limit for each agenda topic 202. Additionally oralternatively, meeting intelligence apparatus 102 can defuse a heatedsituation before it affects the progress of electronic meeting 100.

FIG. 4A is a block diagram that depicts an arrangement for performingspeech or text recognition to determine that audio/video data 300 isrelated to a particular agenda topic. Referring to FIG. 4A, firstmeeting content data 302 includes the speech or text statement “Grosssales are expected to be $10.8 million next quarter.” For example, aparticipant associated with node 106A may have caused first meetingcontent data 302 to be generated by speaking, writing, typing, ordisplaying the statement. Meeting intelligence apparatus 102 includesspeech or text recognition logic 400, which parses first meeting contentdata 302 and detects at least the keywords “next quarter”. The keywordsare a cue 304 for meeting intelligence apparatus 102 to generateintervention data 310 that indicates the appropriate agenda topic. Forexample, intervention data 310 may cause a continued indication of thecurrent agenda topic or cause an indication of a different agenda topic.In the example of FIG. 4A, second meeting content data 312 specifies,among other information, the position of visual indication 200 usingJavaScript Object Notation (JSON). Thus, one or more nodes 106A-N thatprocess the JSON will display visual indication 200 at the specifiedposition in the meeting agenda during electronic meeting 100.

FIG. 4B is a block diagram that depicts an arrangement for performingsentiment analysis to detect an ongoing discussion 402 to beinterrupted. Referring to FIG. 4B, meeting intelligence apparatus 102includes sentiment analysis logic 404, which performs sentiment analysison first meeting content data 302 related to ongoing discussion 402. Forexample, meeting intelligence apparatus 102 may detect an angry tone orsentiment that is a cue 304 for meeting intelligence apparatus 102 togenerate intervention data 310 indicating that another electronicmeeting has been automatically scheduled for continuing ongoingdiscussion 402. In the example of FIG. 4B, second meeting content data312 includes JSON from which scheduling indication 204 can be generatedduring electronic meeting 100.

Meeting intelligence apparatus 102 may use a timer or counter inconjunction with any combination of elements from the foregoingexamples. For example, after meeting intelligence apparatus 102 detectsa discussion of a particular agenda topic, meeting intelligenceapparatus 102 may compare a timer value to a predetermined time limitfor the particular agenda topic. If the timer value exceeds thepredetermined time limit, meeting intelligence apparatus 102 may causescheduling indication 204 to be generated. Additionally oralternatively, meeting intelligence apparatus 102 may cause visualindication 200 of a different agenda topic.

B. Information Retrieval Services

Meeting intelligence apparatus 102 can provide information retrievalservices in a user-friendly manner. Significantly, a participant ofelectronic meeting 100 may formulate an information request in a naturallanguage instead of a computer language, such as Structured QueryLanguage (SQL).

FIG. 4C is a block diagram that depicts an arrangement for retrievingrequested information. Referring to FIG. 4C, meeting intelligenceapparatus 102 receives natural language request 406, which includes thequestion “Where did we leave off at the last meeting?” Note that naturallanguage request 406 may include a question, a statement, a command, orany other type of request for information. Speech or text recognitionlogic 400 parses and interprets first meeting content data 302 to detectnatural language request 406, which is a cue 304 for meetingintelligence apparatus 102 to generate intervention data 310 to be sentto at least node 106A during electronic meeting 100. For example, speechor text recognition logic 400, alone or in combination with sentimentanalysis logic 404, may detect inflected speech and/or keywordsindicative of an information request, such as “who”, “what”, “when”,“where”, “why”, or “how”. Meeting intelligence apparatus 102 caninterpret these and other keywords as commands to perform requestedfunctions, such as data retrieval.

In the example of FIG. 4C, meeting intelligence apparatus 102 mayinterpret the question as a command to search and analyze prior meetingdata to determine an answer to the question. Determining the answer tothe question may involve analyzing meeting content data related to anongoing meeting and/or a prior meeting, thereby increasing the relevancyof the answer to the question. For example, the question “Where did weleave off at the last meeting?” may be analyzed using contextual data(e.g., metadata) from the current meeting, such as the identities ofparticipants 108A-N, the topic of the current discussion, etc. Meetingintelligence apparatus 102 may search the meeting repository forinformation that most closely matches the contextual data from thecurrent meeting. For example, meeting intelligence apparatus 102 maysearch the meeting repository for any prior meetings that included someor all of the participants 108A-N of the current meeting and rank theresults. Meeting intelligence apparatus 102 may then determine that the“last meeting” refers to the top result and may search for the lastagenda topic in the prior meeting that corresponds to the top result.

Intervention data 310 that is generated in response to natural languagerequest 406 includes stored information 410 that meeting intelligenceapparatus 102 retrieves in response to natural language request 406.Meeting intelligence apparatus 102 includes data retrieval logic 408,which performs a search for stored information 410 that is responsive tonatural language request 406. For example, data retrieval logic 408 maysearch a meeting repository and/or external data sources, such aswebsites on the Internet. In the example of FIG. 4C, meetingintelligence apparatus 102 generates second meeting content data 312that includes stored information 410 retrieved from a meetingrepository. The stored information 410 includes the answer to thequestion about a different meeting.

In an embodiment, meeting intelligence apparatus 102 may process naturallanguage request 406 and research a particular topic or otherwise searchfor information that is unrelated to a particular meeting. For example,natural language request 406 may be the statement “We need to figure outhow to get source code from the app.” In response, meeting intelligenceapparatus 102 may retrieve information from various websites thataddress natural language request 406. As shall be described in greaterdetail hereafter, this can be a particularly useful feature forparticipants 108A-N who wish to collaborate, during electronic meeting100, to create a presentation, a report, or any other document.

C. Meeting Content Supplementation

Meeting intelligence apparatus 102 can supplement first meeting contentdata 302 with second meeting content data 312 in any of a number ofways. For example, meeting intelligence apparatus 102 may causeparticipant identifiers to be presented at one or more nodes 106A-N.Additionally or alternatively, meeting intelligence apparatus 102 maycause a language translation or format conversion of first meetingcontent data 302 to be presented at one or more nodes 106A-N.

FIG. 4D is a block diagram that depicts an arrangement for supplementingmeeting content with participant identification data. Referring to FIG.4D, meeting intelligence apparatus 102 includes voice or facerecognition logic 412, which performs voice or face recognition on firstmeeting content data 302 to detect a voice or a face. The voice or faceis a cue 304 for meeting intelligence apparatus 102 to generateintervention data 310 to be sent to at least node 106A during electronicmeeting 100. In response to detecting the cue 304, meeting intelligenceapparatus 102 determines one or more participants 108A-N and generatesparticipant identification data 206 that identifies the one or moreparticipants 108A-N. Meeting intelligence apparatus 102 generates andtransmits second meeting content data 312 that includes participantidentification data 206. When processed at one or more nodes 106A-N,second meeting content data 312 causes participant identification data206 to be presented at the one or more nodes 106A-N.

In an embodiment, meeting intelligence apparatus 102 can perform speechor text recognition on first meeting content data 302 to detect aparticular language, which may be a cue 304 for meeting intelligenceapparatus 102 to generate second meeting content data 312 that includesa translation of first meeting content data 302 into a differentlanguage. For example, meeting intelligence apparatus 102 may translateEnglish content into Japanese content. Second meeting content data 312may replace or supplement first meeting content data 302. For example,second meeting content data 312 may cause Japanese dubbing of firstmeeting content data 302 or may cause Japanese subtitles to be added tofirst meeting content 302.

In an embodiment, meeting intelligence apparatus 102 can detect inputfrom an input/output mechanism, and the input may be a cue 304 formeeting intelligence apparatus 102 to convert the input into a differentformat. For example, the input/output mechanism may be an electronicwhiteboard that receives as input first meeting content data 302 in theform of handwritten notes or hand-drawn illustrations. Based on opticalcharacter recognition (OCR), vector graphics, and/or any other dataconversion tool, meeting intelligence apparatus 102 may convert firstmeeting content data 302 into second meeting content data 312 in theform of machine-lettering or a machine-drawn image. When processed atone or more nodes 106A-N, second meeting content data 312 may cause themachine-lettering or the machine-drawn image to be provided as output onthe electronic whiteboard.

D. Meeting Content Metadata Generation

FIGS. 4A-D each depict second meeting content data 312 that includes avariety of meeting content metadata. Meeting intelligence apparatus 102generates the meeting content metadata based on internal and/or externalinformation. Internal information includes information readilyaccessible to meeting intelligence apparatus 102 even in the absence ofa network connection. For example, if meeting intelligence apparatus 102is a computer, the system date and time are internal information. Incontrast, external information includes information accessible tomeeting intelligence apparatus 102 via a network connection. Forexample, information retrieved from external data sources are externalinformation.

FIGS. 4A-D each depict sending meeting content metadata to one or morenodes 106A-N during electronic meeting 100. However, some meetingcontent metadata may remain untransmitted throughout the duration ofelectronic meeting 100. For example, some meeting content metadata mayremain stored in meeting intelligence apparatus 102 for an internal use,such as generating a report. As shall be described in greater detail inFIG. 6C, a notable example of such meeting content metadata is a labelthat identifies a key meeting point, such as an action item, a task, adeadline, etc.

IV. Post-Processing

Meeting intelligence apparatus 102 can provide any of a number ofservices outside of electronic meeting 100 based on analyzing meetingcontent. Meeting intelligence apparatus 102 may analyze meeting contentat any time relative to electronic meeting 100. For example, afterelectronic meeting 100 ends, meeting intelligence apparatus 102 mayanalyze stored meeting content data and generate a report based onanalyzed meeting content data. Alternatively, meeting intelligenceapparatus 102 may analyze meeting content data during electronic meeting100 and may generate, after electronic meeting 100 ends, a report basedon analyzed meeting content data. The report may be any of a number ofdocuments, such as a meeting agenda, a meeting summary, a meetingtranscript, a meeting participant analysis, a slideshow presentation,etc.

FIG. 5 is a block diagram that depicts an arrangement for generating areport. Referring to FIG. 5, meeting intelligence apparatus 102receives, from node 106A, audio/video data 300 that includes firstmeeting content data 302. Meeting intelligence apparatus 102 includesdata extraction logic 500, metadata generation logic 502, and reportgeneration logic 506. Data extraction logic 500 causes first meetingcontent data 302 to be extracted from audio/video data 300. Meetingintelligence apparatus 102 analyzes first meeting content data 302 anduses metadata generation logic 502 to generate meeting content metadata504. Report generation logic 506 causes meeting content metadata 504 tobe included in report 508.

Meeting intelligence apparatus 102 may do any of a number of things withreport 508. For example, meeting intelligence apparatus 102 may storereport 508 in a meeting repository or provide report 508 to one or morenodes 106A-N associated with participants 108A-N of electronic meeting100. Thus, meeting intelligence apparatus 102 may generate report 508 inan offline mode and/or an online mode.

A. Meeting Content Analysis

In an embodiment, meeting intelligence apparatus 102 generates meetingcontent metadata 504 during electronic meeting 100. For example, datageneration logic 308 may include metadata generation logic 502, andsecond meeting content data 312 may include meeting content metadata504. FIGS. 6A-C depict examples of meeting content metadata 504 that canbe generated during electronic meeting 100.

FIG. 6A is a block diagram that depicts an arrangement for generatingmeeting content metadata 504 that includes participant identificationdata 206. Referring to FIG. 6A, data extraction logic 500 extracts andprovides first meeting content data 302 to metadata generation logic502. In the example of FIG. 6A, metadata generation logic 502 includesvoice or face recognition logic 412, which performs voice or facerecognition on first meeting content data 302 to identify one or moreparticipants 108A-N in electronic meeting 100. Metadata generation logic502 generates meeting content metadata 504 that includes participantidentification data 206 for the one or more participants 108A-N.Metadata generation logic 502 provides meeting content metadata 504 toreport generation logic 506.

FIG. 6B is a block diagram that depicts an arrangement for generatingmeeting content metadata 504 that includes a sentiment detected in firstmeeting content data 302. Referring to FIG. 6B, data extraction logic500 extracts first meeting content data 302 that includes the statement“Not necessarily.” Metadata generation logic 502 includes sentimentanalysis logic 404, which performs sentiment analysis on first meetingcontent data 302 to determine sentiment 600 of a participant inelectronic meeting 100. Meeting generation logic 502 generates meetingcontent metadata 504 that includes sentiment 600. In the example of FIG.6B, meeting content metadata 504 also includes participantidentification data 206 and information related to providing atranslation of first meeting content data 302. Thus, metadata generationlogic 502 can include a combination of sentiment analysis logic 404,voice or face recognition logic 412, and speech or text recognitionlogic 400.

FIG. 6C is a block diagram that depicts an arrangement for generatingmeeting content metadata 504 that includes a label to identify a keymeeting point. Referring to FIG. 6C, first meeting content data 302includes the statement “Action item create schedule by Tuesday”.Metadata generation logic 502 includes speech or text recognition logic400, which performs speech or text recognition on first meeting contentdata 302 to recognize one or more keywords 602 in first meeting contentdata 302. The one or more keywords 602 may indicate a task 604 to becompleted after electronic meeting 100. For example, the one or morekeywords 602 may include a voice or text command to perform a particulartask. In the example of FIG. 6C, the one or more keywords 602 are thelabel “Action item” followed by the command “create schedule byTuesday”. Metadata generation logic 502 generates meeting contentmetadata 504 that includes the one or more keywords 602 and/or the task604.

Meeting intelligence apparatus 102 may generate meeting content metadata504 based on internal and/or external information, such as geolocationinformation or a meeting room availability schedule. In each of FIGS.6A-C, report generation logic 506 includes meeting content metadata 504in report 508. FIGS. 7A-B depict examples of report 508. Referring toFIGS. 7A-B, meeting intelligence apparatus 102 provides report 508 via aweb-based participant interface. Meeting intelligence apparatus 102 maysend report 508 to one or more nodes 106A-N at any of a number of times,such as upon demand, upon detecting a network connection, automaticallyafter each electronic meeting 100, etc.

B. Meeting Summary

FIG. 7A depicts an example meeting summary. In the example of FIG. 7A,report 508 is a meeting summary that includes many of the meetingcontent metadata 504 depicted in FIGS. 6A-C. A meeting summary mayinclude explicit data and/or implicit data. Explicit data includesmeeting content data, such as documents, images, and/or any other dataoriginating from the one or more nodes 106A-N. In the example of FIG.7A, explicit data may include the meeting agenda, the list of “ActionItems”, and/or the list of “Documents”. Implicit data includes meetingcontent metadata 504, such as identifiers, translations, and/or anyother data generated by meeting intelligence apparatus 102. For example,the meeting summary may include a drop-down list providing links to ameeting transcript 700 in the multiple languages depicted in FIG. 6B. Asanother example, the participant identification data 206 depicted inFIG. 6A are provided in the meeting summary as links to individualreports related to each participant. As shall be described in greaterdetail in FIG. 7B, the individual reports may include participantmetrics.

In the example of FIG. 7A, the meeting summary also includes underlinedlinks to other reports, such as the meeting agenda depicted in FIG. 2,task 604 depicted in FIG. 6C, and various documents generated based onone or more input/output mechanisms. For example, the one or moreinput/output mechanisms may include an electronic whiteboard. Meetingintelligence apparatus 102 may convert any handwritten notes orhand-drawn illustrations received as input on the electronic whiteboardinto a machine-lettered or a machine-drawn image based on opticalcharacter recognition (OCR), vector graphics, and/or any other dataconversion tool. For example, meeting intelligence apparatus 102 mayperform OCR on handwritten notes to generate metadata that indicateswhich letters were detected. The metadata may then be used to generatethe detected letters in a particular font or any other machine-letteringformat.

In an embodiment, the meeting summary may include graphical depictionsof meeting efficiency. In the example of FIG. 7A, the meeting summaryincludes a pie chart that details the amount of time spent duringelectronic meeting 100 on each agenda topic 202. FIG. 7A also includes abar representing an efficiency spectrum. An arrow and/or a coloredportion of the bar may indicate the relative position on the bar for aparticular meeting.

C. Participant Analysis

FIG. 7B depicts an example participant analysis. As described above,selecting a particular participant in the meeting summary may cause anindividual report for the selected participant to be presented. Theindividual report may include participation metrics 702 for the selectedparticipant. In the example of FIG. 7B, report 508 is the individualreport “Meeting Participant Profile.” Among the participation metrics702 depicted in FIG. 7B are the amount of participation time for theselected participant, a participation index for the selectedparticipant, a role associated with the selected participant, and a listof timestamped sentiments detected for the selected participant. Theparticipation index may be any measure, weighted or otherwise, of anyaspect of the selected participant's contribution to the meeting. Forexample, “63/100” may indicate a proportion of the total meeting timeduring which the selected participant spoke. The role associated withthe selected participant may indicate any of a number of categories thatdescribe the selected participant relative to the current meeting and/orwithin a particular entity (e.g., a vice-president of a corporation).For example, “Active Presenter” may indicate that the selectedparticipant did not merely respond to other participants, but alsoprovided many of the topics for discussion.

V. Process Overview

FIGS. 8 and 9 are flow diagrams that depict various processes that canbe performed by meeting intelligence apparatus 102. In an embodiment,FIG. 8 depicts a process that is performed with a network connectionduring electronic meeting 100. In an embodiment, FIG. 9 depicts aprocess that can be performed, at least partially, with or without anetwork connection.

A. Generating Intervention Data

FIG. 8 is a flow diagram that depicts an approach for generatingintervention data 310. At block 800, a meeting intelligence apparatus102 receives audio/video data 300 for an electronic meeting 100 thatincludes a plurality of participants 108A-N. The audio/video data 300includes first meeting content data 302 for the electronic meeting 100.For example, Ricoh Brain may receive a videoconference stream from aRicoh UCS P3500 associated with Alice, who is making an offer to Bobduring the electronic meeting 100.

At block 802, the meeting intelligence apparatus 102 determines that theaudio/video data 300 includes a cue 304 for the meeting intelligenceapparatus 102 to intervene in the electronic meeting 100. The meetingintelligence apparatus 102 may make this determination based onperforming any of a number of analyses on the audio/video data 300, suchas speech or text recognition, voice or face recognition, sentimentanalysis, etc. For example, Ricoh Brain may extract and analyze firstmeeting content data 302 to detect poor eye contact by Alice. The pooreye contact may be a cue 304 for Ricoh Brain to respond by sending arecommendation to Bob.

At block 804, the meeting intelligence apparatus 102 generatesintervention data 310 in response to detecting the cue 304. Theintervention data 310 includes second meeting content data 312 that isdifferent from the first meeting content data 302. For example, RicohBrain may generate a recommendation that advises Bob to make acounteroffer.

At block 806, the meeting intelligence apparatus 102 sends theintervention data 310 to one or more nodes 106A-N during the electronicmeeting 100. The one or more nodes 106A-N are associated with at leastone participant of the plurality of participants 108A-N. For example,Ricoh Brain may send the recommendation to Bob and withhold therecommendation from Alice.

B. Generating Reports

FIG. 9 is a flow diagram that depicts an approach for generating areport 508. At block 900, a meeting intelligence apparatus 102 receivesaudio/video data 300 for an electronic meeting 100 that includes aplurality of participants 108A-N. For example, Ricoh Brain may receivean audioconference data packet from Charlie's smartphone, which isexecuting the Ricoh UCS app.

At block 902, the meeting intelligence apparatus 102 extracts meetingcontent data from the audio/video data 300. For example, Ricoh Brain maystrip out header data and analyze the payload of the audioconferencedata packet. Analyzing the payload may involve performing speech or textrecognition, sentiment analysis, voice or face recognition, etc.

At block 904, the meeting intelligence apparatus 102 generates meetingcontent metadata 504 based on analyzing the meeting content data. Forexample, Ricoh Brain may perform voice recognition on the meetingcontent data to identify Charlie as the person presenting at theelectronic meeting 100. Ricoh Brain may generate JSON that includes“speaker: Charlie” among the name-value pairs.

At block 906, the meeting intelligence apparatus 102 includes at leastpart of the meeting content metadata 504 in a report 508 of theelectronic meeting 100. For example, Ricoh Brain may generate a “MeetingSummary” report that includes “Charlie” among the participants 108A-N ofthe electronic meeting 100.

VI. Implementation Mechanisms

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 10 is a block diagram that depicts a computer system1000 upon which an embodiment may be implemented. Computer system 1000includes a bus 1002 or other communication mechanism for communicatinginformation, and a hardware processor 1004 coupled with bus 1002 forprocessing information. Hardware processor 1004 may be, for example, ageneral purpose microprocessor.

Computer system 1000 also includes a main memory 1006, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 1002for storing information and instructions to be executed by processor1004. Main memory 1006 also may be used for storing temporary variablesor other intermediate information during execution of instructions to beexecuted by processor 1004. Such instructions, when stored innon-transitory storage media accessible to processor 1004, rendercomputer system 1000 into a special-purpose machine that is customizedto perform the operations specified in the instructions.

Computer system 1000 further includes a read only memory (ROM) 1008 orother static storage device coupled to bus 1002 for storing staticinformation and instructions for processor 1004. A storage device 1010,such as a magnetic disk or optical disk, is provided and coupled to bus1002 for storing information and instructions.

Computer system 1000 may be coupled via bus 1002 to a display 1012, suchas a cathode ray tube (CRT), for displaying information to a computeruser. An input device 1014, including alphanumeric and other keys, iscoupled to bus 1002 for communicating information and command selectionsto processor 1004. Another type of user input device is cursor control1016, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor1004 and for controlling cursor movement on display 1012. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane.

Computer system 1000 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 1000 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 1000 in response to processor 1004 executing one or moresequences of one or more instructions contained in main memory 1006.Such instructions may be read into main memory 1006 from another storagemedium, such as storage device 1010. Execution of the sequences ofinstructions contained in main memory 1006 causes processor 1004 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 1010.Volatile media includes dynamic memory, such as main memory 1006. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 1002. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 1004 for execution. Forexample, the instructions may initially be carried on a magnetic disk orsolid state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 1000 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 1002. Bus 1002 carries the data tomain memory 1006, from which processor 1004 retrieves and executes theinstructions. The instructions received by main memory 1006 mayoptionally be stored on storage device 1010 either before or afterexecution by processor 1004.

Computer system 1000 also includes a communication interface 1018coupled to bus 1002. Communication interface 1018 provides a two-waydata communication coupling to a network link 1020 that is connected toa local network 1022. For example, communication interface 1018 may bean integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 1018 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN. Wirelesslinks may also be implemented. In any such implementation, communicationinterface 1018 sends and receives electrical, electromagnetic or opticalsignals that carry digital data streams representing various types ofinformation.

Network link 1020 typically provides data communication through one ormore networks to other data devices. For example, network link 1020 mayprovide a connection through local network 1022 to a host computer 1024or to data equipment operated by an Internet Service Provider (ISP)1026. ISP 1026 in turn provides data communication services through theworld wide packet data communication network now commonly referred to asthe “Internet” 1028. Local network 1022 and Internet 1028 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 1020 and through communication interface 1018, which carrythe digital data to and from computer system 1000, are example forms oftransmission media.

Computer system 1000 can send messages and receive data, includingprogram code, through the network(s), network link 1020 andcommunication interface 1018. In the Internet example, a server 1030might transmit a requested code for an application program throughInternet 1028, ISP 1026, local network 1022 and communication interface1018.

The received code may be executed by processor 1004 as it is received,and/or stored in storage device 1010, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the disclosure, and what isintended by the applicants to be the scope of the disclosure, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

What is claimed is:
 1. An apparatus comprising: one or more processors;and one or more non-transitory computer-readable media storinginstructions which, when processed by the one or more processors, cause:receiving, at the apparatus, first meeting content data for anelectronic meeting that includes one or more participants; determining,by the apparatus, that the first meeting content data for the electronicmeeting that includes one or more participants includes one or moreactions items; in response to the determining, by the apparatus, thatthe first meeting content data for the electronic meeting that includesone or more participants includes one or more actions items, generatingmeeting content metadata that specifies the one or more action items. 2.The apparatus of claim 1, wherein the one or more non-transitorycomputer-readable media store additional instructions which, whenprocessed by the one or more processors, cause one or more of:transmitting the meeting content metadata to a node associated with aparticipant of electronic meeting, including the meeting contentmetadata in a meeting summary or meeting report for the electronicmeeting, or providing the meeting content metadata to an external datasource.
 3. The apparatus of claim 1, wherein the one or morenon-transitory computer-readable media store additional instructionswhich, when processed by the one or more processors, cause: generatingsecond meeting content data and including, in the second meeting contentdata, the meeting content metadata that specifies the one or more actionitems; and transmitting the second meeting content metadata to a nodeassociated with a participant of electronic meeting.
 4. The apparatus ofclaim 1, wherein: the first meeting content data includes audio/videodata, and determining, by the apparatus, that the first meeting contentdata for the electronic meeting that includes one or more participantsincludes one or more actions items includes identifying, by theapparatus, text contained in the audio/video data.
 5. The apparatus ofclaim 4, wherein the text is identified by one or more of speech or textrecognition, or text formatting. Similar comment as above.
 6. Theapparatus of claim 4, wherein the text includes a label and a command.7. The apparatus of claim 6, wherein the command is a task to becompleted.
 8. The apparatus of claim 7, wherein the task to be completedis included in a meeting summary or meeting report for the electronicmeeting.
 9. The apparatus of claim 8, wherein the one or morenon-transitory computer-readable media store additional instructionswhich, when processed by the one or more processors, cause transmittingthe meeting summary or meeting report for the electronic meeting to anode associated with a participant of electronic meeting.
 10. Theapparatus of claim 1, wherein: the first meeting content data includesone or more of hand-written notes or hand-drawn illustrations, anddetermining, by the apparatus, that the first meeting content data forthe electronic meeting that includes one or more participants includesone or more actions items includes identifying, by the apparatus, theone or more action items from one or more of the hand-written notes orthe hand-drawn illustrations.
 11. One or more non-transitorycomputer-readable media storing instructions which, when processed byone or more processors, cause: receiving, at an apparatus, first meetingcontent data for an electronic meeting that includes one or moreparticipants; determining, by the apparatus, that the first meetingcontent data for the electronic meeting that includes one or moreparticipants includes one or more actions items; in response to thedetermining, by the apparatus, that the first meeting content data forthe electronic meeting that includes one or more participants includesone or more actions items, generating meeting content metadata thatspecifies the one or more action items.
 12. The one or morenon-transitory computer-readable media of claim 11, further comprisingadditional instructions which, when processed by the one or moreprocessors, cause one or more of: transmitting the meeting contentmetadata to a node associated with a participant of electronic meeting,including the meeting content metadata in a meeting summary or meetingreport for the electronic meeting, or providing the meeting contentmetadata to an external data source.
 13. The one or more non-transitorycomputer-readable media of claim 11, further comprising additionalinstructions which, when processed by the one or more processors, cause:generating second meeting content data and including, in the secondmeeting content data, the meeting content metadata that specifies theone or more action items; and transmitting the second meeting contentmetadata to a node associated with a participant of electronic meeting.14. The one or more non-transitory computer-readable media of claim 11,wherein: the first meeting content data includes audio/video data, anddetermining, by the apparatus, that the first meeting content data forthe electronic meeting that includes one or more participants includesone or more actions items includes identifying, by the apparatus, one ormore recognized keywords contained in the audio/video data.
 15. The oneor more non-transitory computer-readable media of claim 14, wherein therecognized keywords are identified by speech or text recognition. 16.The one or more non-transitory computer-readable media of claim 14,wherein the recognized keywords include a label and a command.
 17. Theone or more non-transitory computer-readable media of claim 16, whereinthe command is a task to be completed.
 18. The one or morenon-transitory computer-readable media of claim 17, wherein the task tobe completed is included in a meeting summary or meeting report for theelectronic meeting.
 19. The one or more non-transitory computer-readablemedia of claim 18, further comprising additional instructions which,when processed by the one or more processors, cause transmitting themeeting summary or meeting report for the electronic meeting to a nodeassociated with a participant of electronic meeting.
 20. Acomputer-implemented method comprising: receiving, at an apparatus,first meeting content data for an electronic meeting that includes oneor more participants; determining, by the apparatus, that the firstmeeting content data for the electronic meeting that includes one ormore participants includes one or more actions items; in response to thedetermining, by the apparatus, that the first meeting content data forthe electronic meeting that includes one or more participants includesone or more actions items, generating meeting content metadata thatspecifies the one or more action items.